Differences in Kaizen implementation between countries and industry types in multinational supply chain
Bibliographic record
Abstract
Previous research shows that Kaizen's benefits are multiple and evident, but its practices in the supply chain have been sufficiently examined now. Conversely, we are witnessing numerous issues in contemporary global supply networks. In this survey, after conducting a literature review, three research questions regarding Kaizen modes of usage were formulated and tested on the sample of 195 enterprises that are part of the global supply chain, located in 31 countries, and active in two different types of industries - aircraft, and transportation. A combined approach containing descriptive statistics, reliability, factor analysis, and statistical hypothesis testing by Kruskal-Wallis one-way ANOVA and Mann-Whitney U tests were used. Results show significant differences between Kaizen practices applied in countries such as Italy, the United Kingdom, Canada, the USA, Japan, and China, where national and corporate cultures differ. Kaizen implementation significantly differs between companies operating in the aircraft and transportation sectors, which is unsurprising since aircraft industry has a higher formalization level. The goal to determine the differences in Kaizen practices around the globe was fulfilled since statistically significant differences indicate the importance of the contextual factors and connect adverse and Kaizen events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".